Another related approach was proposed by [1]}. In this work, a relative form of ground truth is created by randomizing the parameter values of the network, layer by layer. The assumption is that the feature attribution map should be significantly different for a trained model vs. a randomized model. Methods that return the same attribution map for both models, appear to be independent of the model parameters. This approach however does not provide a numerical value that captures the “quality” of the explanation, but rather acts as a pass/fail “sanity check”, and is also not considered in this work.
